{"id":553,"date":"2025-07-24T10:09:41","date_gmt":"2025-07-24T14:09:41","guid":{"rendered":"https:\/\/sites.wp.odu.edu\/t3-ciders\/?p=553"},"modified":"2025-09-11T17:33:26","modified_gmt":"2025-09-11T21:33:26","slug":"2025-deapsecure-deep-learning-pilot-workshop","status":"publish","type":"post","link":"https:\/\/sites.wp.odu.edu\/t3-ciders\/2025\/07\/24\/2025-deapsecure-deep-learning-pilot-workshop\/","title":{"rendered":"2025 DeapSECURE &#8220;Deep Learning&#8221; Pilot Workshop"},"content":{"rendered":"\n<p>We are excited to announce that we are offering a pilot workshop for our <span style=\"text-decoration: underline\">newly revamped<\/span> <a href=\"https:\/\/deapsecure.gitlab.io\/deapsecure-lesson04-nn\/\">DeapSECURE Deep Learning lesson<\/a> (module 4). This workshop features both lecture-based and hands-on coding activities that utilize a <strong>high-performance computing (HPC)<\/strong> environment. The material includes an introduction to <strong>machine learning<\/strong> and <strong>deep learning<\/strong>, a case study that applies deep learning for <strong>cybersecurity <\/strong>(app classifier based on SherLock dataset), an overview of <strong>neural network concepts, model tuning using HPC<\/strong>, and further applications of deep learning in the real world. <\/p>\n\n\n\n<p>Techniques taught in DeapSECURE workshops are foundational and transferable to other areas, including science, engineering, finance, linguistics, etc. (<a href=\"https:\/\/deapsecure.gitlab.io\/workshops#why-cybersecurity\">Why Cybersecurity?<\/a>)<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What You\u2019ll Learn:<\/h2>\n\n\n\n<ul>\n<li>Basics of machine learning and deep learning<\/li>\n\n\n\n<li>Building and tuning neural networks with Keras<\/li>\n\n\n\n<li>Using ODU\u2019s HPC system for model training<\/li>\n\n\n\n<li>Visualizing and analyzing deep learning results<\/li>\n\n\n\n<li>Real-world application: Mobile device cybersecurity<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Workshop Audience<\/h2>\n\n\n\n<p>This training program is aimed at (1) students with interests in cutting-edge cybersecurity research, (2) those who need to use deep learning techniques for their research projects, and\/or (3) those who want to learn how to harness the power of HPC through a realistic example.<\/p>\n\n\n\n<p><strong>Basic skill of writing computer programs is required to participate in this training<\/strong>. The specific language matters less; it is the programming skill that matters. Popular languages such as C, C++, Java, JavaScript, Python, R, and Matlab would be fine. This workshop will be using Python programming language and TensorFlow\/Keras deep learning framework.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Schedule<\/h2>\n\n\n\n<p>This summer workshop will take place on <strong>August 11th from 1pm &#8211; 5pm and August 12th from 9am &#8211; 5pm<\/strong> at Old Dominion University (ODU) in Norfolk, VA, and will also be offered through Zoom.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Date &amp; Time<\/strong><\/td><td><strong>Lesson Episodes<\/strong><\/td><\/tr><tr><td>August 11, 2025 | 1pm &#8211; 5pm<\/td><td><a href=\"https:\/\/deapsecure.gitlab.io\/deapsecure-lesson04-nn\/01-introduction\/index.html\"><em>Introduction to Machine Learning &amp; Deep Learning<\/em><\/a><br><a href=\"https:\/\/deapsecure.gitlab.io\/deapsecure-lesson04-nn\/02-sherlock-apps-ml\/index.html\"><em>Deep Learning to Identify Smartphone Applications<\/em><\/a><br><a href=\"https:\/\/deapsecure.gitlab.io\/deapsecure-lesson04-nn\/10-neural-network-concepts\/index.html\"><em>Overview of Deep Neural Network Concepts<\/em><\/a><br><a href=\"https:\/\/deapsecure.gitlab.io\/deapsecure-lesson04-nn\/24-keras-classify\/index.html\"><em>Classifying Smartphone Apps with Keras<\/em><\/a><\/td><\/tr><tr><td>August 12, 2025 | 9am &#8211; 5pm<\/td><td><a href=\"https:\/\/deapsecure.gitlab.io\/deapsecure-lesson04-nn\/30-model-tuning\/index.html\"><em>Tuning Neural Network Models for Better Accuracy<\/em><\/a><br><a href=\"https:\/\/deapsecure.gitlab.io\/deapsecure-lesson04-nn\/31-batch-tuning-hpc\/index.html\"><em>Effective Deep Learning Workflow on HPC<\/em><\/a><br><a href=\"https:\/\/deapsecure.gitlab.io\/deapsecure-lesson04-nn\/32-analysis-tuning\/index.html\"><em>Post-Analysis for Modeling Tuning Experiments<\/em><\/a><br><a href=\"https:\/\/deapsecure.gitlab.io\/deapsecure-lesson04-nn\/99-outro-neural-networks\/index.html\"><em>Deep Learning in the Real World<\/em><\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Format<\/h2>\n\n\n\n<p>The workshop will be conducted in-person at Old Dominion University campus and virtually over Zoom. (Whenever possible, in-person participation is strongly encouraged.) It will consist of a mix of lectures \/ hands-on activities to introduce learners to basic concepts and practical skills in HPC, Machine Learning, and Neural Networks. Materials will be introduced at an&nbsp;<strong>introductory level<\/strong>. Hands-on activities will be carried out using&nbsp;common tools such as <strong>Python<\/strong>, <strong>Jupyter notebooks<\/strong>, <strong>UNIX<\/strong> <strong>shell<\/strong>, on ODU&#8217;s <strong>Wahab HPC<\/strong>. There will be teaching assistants to help the learners.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Rules &amp; Requirements<\/h2>\n\n\n\n<ol>\n<li><mark style=\"background-color:#ffd100\" class=\"has-inline-color\">This is a pilot workshop of a new lesson; expect some rough edges as we will be teaching this lesson for the first time.<\/mark> <\/li>\n\n\n\n<li>We expect learners to engage seriously with the learning materials during the workshop, and will provide us honest feedback after the workshop, so we can improve the lesson.<\/li>\n\n\n\n<li>This is a hands-on training. You must bring and use your computer to connect to and perform exercises on ODU\u2019s HPC cluster. If you are attending virtually, the same requirements apply.<\/li>\n\n\n\n<li>The two-day workshops build upon one another! If you sign up and are accepted, you must participate in both days.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">Survey Disclosure<\/h2>\n\n\n\n<p>Assessments and participants\u2019 feedback are an integral part of this NSF-funded training program; we require that all participants fill both the pre- and post-workshop surveys. By participating in this training, you agree to respond to our pre- and post-workshop surveys. Your responses will remain anonymous and confidential. We will use your collective responses and feedback to help us improve our lessons. Any published statistics will be reported in a manner that preserves the privacy of individual responses.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Sign-up<\/h2>\n\n\n\n<p>You can register for the workshop using the following informal preliminary sign-up sheet <a href=\"https:\/\/docs.google.com\/spreadsheets\/d\/1HPn99KA1tlWeE5R96dlns4ICJZckxrwPVtpgEutlCYY\/edit?usp=sharing\">here<\/a>. We may reach back to you to request for clarification\/information regarding your registration, if necessary. You will be notified before the workshop starts whether you are accepted into the training program. The pre-workshop survey will be sent after admittance to the workshop is confirmed.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Have Questions?<\/h2>\n\n\n\n<p>If you have questions regarding this workshop, please send an email to <a href=\"mailto:t3ciders@gmail.com\">t3ciders@gmail.com<\/a> .<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Acknowledgements<\/h2>\n\n\n\n<p>This training project is funded by the U.S. National Science Foundation CyberTraining grants #2320998 and #2320999.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We are excited to announce that we are offering a pilot workshop for our newly revamped DeapSECURE Deep Learning lesson (module 4). This workshop features both lecture-based and hands-on coding activities that utilize a high-performance computing (HPC) environment. The material&#8230; <a class=\"more-link\" href=\"https:\/\/sites.wp.odu.edu\/t3-ciders\/2025\/07\/24\/2025-deapsecure-deep-learning-pilot-workshop\/\">Continue Reading &rarr;<\/a><\/p>\n","protected":false},"author":26536,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","wds_primary_category":15},"categories":[16,1,15],"tags":[],"_links":{"self":[{"href":"https:\/\/sites.wp.odu.edu\/t3-ciders\/wp-json\/wp\/v2\/posts\/553"}],"collection":[{"href":"https:\/\/sites.wp.odu.edu\/t3-ciders\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.wp.odu.edu\/t3-ciders\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.wp.odu.edu\/t3-ciders\/wp-json\/wp\/v2\/users\/26536"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.wp.odu.edu\/t3-ciders\/wp-json\/wp\/v2\/comments?post=553"}],"version-history":[{"count":4,"href":"https:\/\/sites.wp.odu.edu\/t3-ciders\/wp-json\/wp\/v2\/posts\/553\/revisions"}],"predecessor-version":[{"id":568,"href":"https:\/\/sites.wp.odu.edu\/t3-ciders\/wp-json\/wp\/v2\/posts\/553\/revisions\/568"}],"wp:attachment":[{"href":"https:\/\/sites.wp.odu.edu\/t3-ciders\/wp-json\/wp\/v2\/media?parent=553"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.wp.odu.edu\/t3-ciders\/wp-json\/wp\/v2\/categories?post=553"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.wp.odu.edu\/t3-ciders\/wp-json\/wp\/v2\/tags?post=553"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}